Traditional studies on quality estimation are based on handcrafted features [1]}, [2]}, [3]} or feature selection [4]}, [5]}.
Deep learning methods have also been applied to quality estimation [6]}, [7]}.
The recent Predictor-Estimator framework [8]} for QE has drawn much attention, which consists of an RNN word prediction model firstly trained on a large amount of parallel corpora for feature extractions, and an estimator that is subsequently trained on a smaller amount of sentence pairs with QE labels and provides quality evaluation based on the extracted features.
[9]} [9]} base their work on the predictor-estimator framework and further propose to substitute the RNN predictor with a bidirectional transformer language model.
